Merged.

Merge branch 'master' of https://version.helsinki.fi/rikulain/bachelors-thesis

paper/related.tex

@@ -16,6 +16,9 @@ In our simulations we compared in particular to \contraction of~\citet{lakkaraju
 However, as our experiments confirm, it is quite sensitive to the number of subjects assigned to (the most) lenient decision makers.
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 In addition, \citet{kleinberg2018human} present an in-detail account of employing \contraction in a real data.
+%
+In their experiments, they use a decision maker that is setup similarly to \independent decision makers discussed in our work, but who make decisions not based on a leniency, but a threshold determined by cost or utility values.
+
 
 In contrast to our imputation approach, De-Arteaga et al.~\cite{dearteaga2018learning} directly impute decisions as outcomes and consider learning automatic decision makers from such augmented data. 
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@@ -31,7 +34,13 @@ Mc-Candless et al. perform Bayesian sensitivity analysis while taking into accou
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 \citet{kallus2018confounding} obtain improved policies from data possibly biased by a baseline policy.
 
-The importance in-detail causal modeling and evaluating counterfactual outcomes, as observed also here, is particularly prominent in recent work on fairness of automatic decision making~\cite{DBLP:conf/icml/NabiMS19,DBLP:conf/icml/Kusner0LS19,coston2020counterfactual,madras2019fairness,corbett2017algorithmic,DBLP:journals/jmlr/BottouPCCCPRSS13,DBLP:conf/icml/JohanssonSS16}. Several author study selection bias or missing data in the context of identifiability of causal effects and causal structure~\cite{bareinboim2012controlling,hernan2004structural,little2019statistical,Bareinboim2014:selectionbias,smr1999,Mohan2013,Shpitser2015}.
+
+The importance of in-detail causal modeling and evaluating counterfactual outcomes, as observed also here, is particularly prominent in recent work on fairness of automatic decision making~\cite{DBLP:conf/icml/NabiMS19,DBLP:conf/icml/Kusner0LS19,coston2020counterfactual,madras2019fairness,corbett2017algorithmic,DBLP:journals/jmlr/BottouPCCCPRSS13,DBLP:conf/icml/JohanssonSS16}. 
+%
+Several works study selection bias or missing data in the context of identifiability of causal effects and causal structure~\cite{bareinboim2012controlling,hernan2004structural,little2019statistical,Bareinboim2014:selectionbias,smr1999,Mohan2013,Shpitser2015}.
+%
+%Also identifiability questions in the presence of selection bias or missing data mechanisms require detailed causal modeling~\cite{bareinboim2012controlling,hernan2004structural,little2019statistical}.
+
 %To properly assess decision procedures for their performance and fairness we need to understand the causal relations 
 Finally, more applied work on automated decision making and risk scoring, related in particular to recidivism, can be found for example in~\cite{murder,tolan2019why,kleinberg2018human,chouldechova2017fair,brennan2009evaluating}.